{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 机器学习工程师纳米学位\n",
    "## 模型评价与验证\n",
    "## 项目 1: 预测波士顿房价\n",
    "\n",
    "\n",
    "欢迎来到机器学习工程师纳米学位的第一个项目！在此文件中，有些示例代码已经提供给你，但你还需要实现更多的功能来让项目成功运行。除非有明确要求，你无须修改任何已给出的代码。以**编程练习**开始的标题表示接下来的内容中有需要你必须实现的功能。每一部分都会有详细的指导，需要实现的部分也会在注释中以**TODO**标出。请仔细阅读所有的提示！\n",
    "\n",
    "除了实现代码外，你还**必须**回答一些与项目和实现有关的问题。每一个需要你回答的问题都会以**'问题 X'**为标题。请仔细阅读每个问题，并且在问题后的**'回答'**文字框中写出完整的答案。你的项目将会根据你对问题的回答和撰写代码所实现的功能来进行评分。\n",
    "\n",
    ">**提示：**Code 和 Markdown 区域可通过 **Shift + Enter** 快捷键运行。此外，Markdown可以通过双击进入编辑模式。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---\n",
    "## 第一步. 导入数据\n",
    "在这个项目中，你将利用马萨诸塞州波士顿郊区的房屋信息数据训练和测试一个模型，并对模型的性能和预测能力进行测试。通过该数据训练后的好的模型可以被用来对房屋做特定预测---尤其是对房屋的价值。对于房地产经纪等人的日常工作来说，这样的预测模型被证明非常有价值。\n",
    "\n",
    "此项目的数据集来自[UCI机器学习知识库(数据集已下线)](https://archive.ics.uci.edu/ml/datasets.html)。波士顿房屋这些数据于1978年开始统计，共506个数据点，涵盖了麻省波士顿不同郊区房屋14种特征的信息。本项目对原始数据集做了以下处理：\n",
    "- 有16个`'MEDV'` 值为50.0的数据点被移除。 这很可能是由于这些数据点包含**遗失**或**看不到的值**。\n",
    "- 有1个数据点的 `'RM'` 值为8.78. 这是一个异常值，已经被移除。\n",
    "- 对于本项目，房屋的`'RM'`， `'LSTAT'`，`'PTRATIO'`以及`'MEDV'`特征是必要的，其余不相关特征已经被移除。\n",
    "- `'MEDV'`特征的值已经过必要的数学转换，可以反映35年来市场的通货膨胀效应。\n",
    "\n",
    "运行下面区域的代码以载入波士顿房屋数据集，以及一些此项目所需的Python库。如果成功返回数据集的大小，表示数据集已载入成功。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# 载入此项目所需要的库\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import visuals as vs # Supplementary code\n",
    "\n",
    "# 检查你的Python版本\n",
    "from sys import version_info\n",
    "if version_info.major != 2 and version_info.minor != 7:\n",
    "    raise Exception('请使用Python 2.7来完成此项目')\n",
    "    \n",
    "# 让结果在notebook中显示\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Boston housing dataset has 489 data points with 4 variables each.\n"
     ]
    }
   ],
   "source": [
    "# 载入波士顿房屋的数据集\n",
    "data = pd.read_csv('housing.csv')\n",
    "prices = data['MEDV']\n",
    "features = data.drop('MEDV', axis = 1)\n",
    "    \n",
    "# 完成\n",
    "print \"Boston housing dataset has {} data points with {} variables each.\".format(*data.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---\n",
    "## 第二步. 分析数据\n",
    "在项目的第一个部分，你会对波士顿房地产数据进行初步的观察并给出你的分析。通过对数据的探索来熟悉数据可以让你更好地理解和解释你的结果。\n",
    "\n",
    "由于这个项目的最终目标是建立一个预测房屋价值的模型，我们需要将数据集分为**特征(features)**和**目标变量(target variable)**。\n",
    "- **特征** `'RM'`， `'LSTAT'`，和 `'PTRATIO'`，给我们提供了每个数据点的数量相关的信息。\n",
    "- **目标变量**：` 'MEDV'`，是我们希望预测的变量。\n",
    "\n",
    "他们分别被存在`features`和`prices`两个变量名中。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 编程练习 1：基础统计运算\n",
    "你的第一个编程练习是计算有关波士顿房价的描述统计数据。我们已为你导入了` numpy `，你需要使用这个库来执行必要的计算。这些统计数据对于分析模型的预测结果非常重要的。\n",
    "在下面的代码中，你要做的是：\n",
    "- 计算`prices`中的`'MEDV'`的最小值、最大值、均值、中值和标准差；\n",
    "- 将运算结果储存在相应的变量中。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Statistics for Boston housing dataset:\n",
      "\n",
      "Minimum price: $105,000.00\n",
      "Maximum price: $1,024,800.00\n",
      "Mean price: $454,342.94\n",
      "Median price $438,900.00\n",
      "Standard deviation of prices: $165,171.13\n"
     ]
    }
   ],
   "source": [
    "#TODO 1\n",
    "\n",
    "#目标：计算价值的最小值\n",
    "minimum_price = np.min(prices)\n",
    "\n",
    "#目标：计算价值的最大值\n",
    "maximum_price = np.max(prices)\n",
    "\n",
    "#目标：计算价值的平均值\n",
    "mean_price = np.mean(prices)\n",
    "\n",
    "#目标：计算价值的中值\n",
    "median_price = np.median(prices)\n",
    "\n",
    "#目标：计算价值的标准差\n",
    "std_price = np.std(prices)\n",
    "\n",
    "#目标：输出计算的结果\n",
    "print \"Statistics for Boston housing dataset:\\n\"\n",
    "print \"Minimum price: ${:,.2f}\".format(minimum_price)\n",
    "print \"Maximum price: ${:,.2f}\".format(maximum_price)\n",
    "print \"Mean price: ${:,.2f}\".format(mean_price)\n",
    "print \"Median price ${:,.2f}\".format(median_price)\n",
    "print \"Standard deviation of prices: ${:,.2f}\".format(std_price)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 问题 1 - 特征观察\n",
    "\n",
    "如前文所述，本项目中我们关注的是其中三个值:`'RM'`、`'LSTAT'` 和`'PTRATIO'`，对每一个数据点:\n",
    "- `'RM'` 是该地区中每个房屋的平均房间数量；\n",
    "- `'LSTAT'` 是指该地区有多少百分比的房东属于是低收入阶层（有工作但收入微薄）；\n",
    "- `'PTRATIO'` 是该地区的中学和小学里，学生和老师的数目比（`学生/老师`）。\n",
    "\n",
    "_凭直觉，上述三个特征中对每一个来说，你认为增大该特征的数值，`'MEDV'`的值会是**增大**还是**减小**呢？每一个答案都需要你给出理由。_\n",
    "\n",
    "**提示：**你预期一个`'RM'` 值是6的房屋跟`'RM'` 值是7的房屋相比，价值更高还是更低呢？"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PathCollection at 0x7f7f7d42a150>"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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EujWXocAeH93kE2uWZzKuT6/4ivL0m+0HVDVYJCJPVWO7MZfbx/fwtR2vtuVc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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f7fbde17690>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(features['RM'], prices)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PathCollection at 0x7f7f7d350210>"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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WsiYVxml4SRf9ME1XWfUiL1LnmZjZrwEvOOf2mtnb8ptSfpjZDcANACtWrCh4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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f7f7d472250>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(features['LSTAT'], prices)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PathCollection at 0x7f7f7d2cf0d0>"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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KlVWijSp2K+1LL66ZxETEJg9TQ6fNFa0RY1PO8dDmcUZPnec9bjsTU9gTXicy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\nITLz/wGgl33u8P+AIgAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f7f7d392490>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(features['PTRATIO'], prices)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 问题 1 - 回答：\n",
    "1. 图中虽然有小部分点RM小MEDV大，但是绝大部分的趋势还是RM大，MEDV大。现实中，房间越多也往往意味着面积越大，随之房价也高，与实际相符。所以RM增大，‘MEDV’增大。\n",
    "2. MEDV大的点都集中在左侧LSTAT较小的区域，整个趋势也是下降的。结合现实，收入较低的房东，持有的房子也相对廉价，高收入人群往往能挑选到更好地段的房屋，并进行更加精致的装修，因而趋势和实际情况也相符。所以LSTAT增大，‘MEDV’减小。\n",
    "3. 图中点比较散，很难看出趋势。考虑到这个特征的实际意义，每个老师对应的学生越少，分给每个学生的精力就多，教学质量相应地也会提高，好学校也会拉动周围的房价上涨。再看图，最左侧点比最右侧点明显偏高，所以PTRATIO增大，‘MEDV’减小。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 编程练习 2: 数据分割与重排\n",
    "接下来，你需要把波士顿房屋数据集分成训练和测试两个子集。通常在这个过程中，数据也会被重排列，以消除数据集中由于顺序而产生的偏差。\n",
    "在下面的代码中，你需要\n",
    "\n",
    "使用 `sklearn.model_selection` 中的 `train_test_split`， 将`features`和`prices`的数据都分成用于训练的数据子集和用于测试的数据子集。\n",
    "  - 分割比例为：80%的数据用于训练，20%用于测试；\n",
    "  - 选定一个数值以设定 `train_test_split` 中的 `random_state` ，这会确保结果的一致性；"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# TODO 2\n",
    "\n",
    "# 提示： 导入train_test_split\n",
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "X_train, X_test, y_train, y_test = train_test_split(features, prices, test_size = 0.2, random_state = 18)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 问题 2 - 训练及测试\n",
    "*将数据集按一定比例分为训练用的数据集和测试用的数据集对学习算法有什么好处？*\n",
    "\n",
    "*如果用模型已经见过的数据，例如部分训练集数据进行测试，又有什么坏处？*\n",
    "\n",
    "**提示：** 如果没有数据来对模型进行测试，会出现什么问题？"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 问题 2 - 回答:\n",
    "#### 测试数据可以更好地反映模型的泛化能力。\n",
    "\n",
    "模型在学习训练数据时，会同时学习到这类事物的特征，和训练数据集特有的特征。\n",
    "\n",
    "理想的情况是只学习到这类事物的特征，但是实际情况中或多或少会学习到训练数据集特有的特征。如果只用部分训练集数据进行测试，就算测试出来准确率很高，也可能是对训练数据集特有特征学习得好，但是对这类事物的特征学习得不好，造成了过拟合。\n",
    "\n",
    "而用模型没有见过的测试用数据集，就可以看出模型对于该事物通用特征的学习程度， 更接近实际应用的状况，这样的准确度更有参考性。\n",
    "\n",
    "打一个比方，训练用数据集就是除去年外历届高考题，测试用数据集就是去年高考题。如果已经做过除去年外历届高考题，再用这些题测试学习水平的话，虽然测试成绩很好看，但不能反映真实的水平。而用从来没有做过的去年高考题测试，就可以反映出真实的水平，这个才可以作为真正高考的参考依据。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---\n",
    "## 第三步. 模型衡量标准\n",
    "在项目的第三步中，你需要了解必要的工具和技巧来让你的模型进行预测。用这些工具和技巧对每一个模型的表现做精确的衡量可以极大地增强你预测的信心。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 编程练习3：定义衡量标准\n",
    "如果不能对模型的训练和测试的表现进行量化地评估，我们就很难衡量模型的好坏。通常我们会定义一些衡量标准，这些标准可以通过对某些误差或者拟合程度的计算来得到。在这个项目中，你将通过运算[*决定系数*](http://stattrek.com/statistics/dictionary.aspx?definition=coefficient_of_determination) R<sup>2</sup> 来量化模型的表现。模型的决定系数是回归分析中十分常用的统计信息，经常被当作衡量模型预测能力好坏的标准。\n",
    "\n",
    "R<sup>2</sup>的数值范围从0至1，表示**目标变量**的预测值和实际值之间的相关程度平方的百分比。一个模型的R<sup>2</sup> 值为0还不如直接用**平均值**来预测效果好；而一个R<sup>2</sup> 值为1的模型则可以对目标变量进行完美的预测。从0至1之间的数值，则表示该模型中目标变量中有百分之多少能够用**特征**来解释。_模型也可能出现负值的R<sup>2</sup>，这种情况下模型所做预测有时会比直接计算目标变量的平均值差很多。_\n",
    "\n",
    "在下方代码的 `performance_metric` 函数中，你要实现：\n",
    "- 使用 `sklearn.metrics` 中的 [`r2_score`](http://scikit-learn.org/stable/modules/generated/sklearn.metrics.r2_score.html) 来计算 `y_true` 和 `y_predict`的R<sup>2</sup>值，作为对其表现的评判。\n",
    "- 将他们的表现评分储存到`score`变量中。\n",
    "\n",
    "或 \n",
    "\n",
    "- (可选) 不使用任何外部库，参考[决定系数的定义](https://en.wikipedia.org/wiki/Coefficient_of_determination)进行计算，这也可以帮助你更好的理解决定系数在什么情况下等于0或等于1。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# TODO 3\n",
    "\n",
    "# 提示： 导入r2_score\n",
    "\n",
    "from sklearn.metrics import r2_score\n",
    "\n",
    "def performance_metric(y_true, y_predict):\n",
    "    \"\"\"计算并返回预测值相比于预测值的分数\"\"\"\n",
    "    \n",
    "    score = r2_score(y_true, y_predict)\n",
    "\n",
    "    return score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# TODO 3 可选\n",
    "\n",
    "# 不允许导入任何计算决定系数的库\n",
    "\n",
    "def performance_metric2(y_true, y_predict):\n",
    "    \"\"\"计算并返回预测值相比于预测值的分数\"\"\"\n",
    "    \n",
    "    score = None\n",
    "\n",
    "    return score"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 问题 3 - 拟合程度\n",
    "\n",
    "假设一个数据集有五个数据且一个模型做出下列目标变量的预测：\n",
    "\n",
    "| 真实数值 | 预测数值 |\n",
    "| :-------------: | :--------: |\n",
    "| 3.0 | 2.5 |\n",
    "| -0.5 | 0.0 |\n",
    "| 2.0 | 2.1 |\n",
    "| 7.0 | 7.8 |\n",
    "| 4.2 | 5.3 |\n",
    "*你觉得这个模型已成功地描述了目标变量的变化吗？如果成功，请解释为什么，如果没有，也请给出原因。*  \n",
    "\n",
    "**提示**：运行下方的代码，使用`performance_metric`函数来计算模型的决定系数。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model has a coefficient of determination, R^2, of 0.923.\n"
     ]
    }
   ],
   "source": [
    "# 计算这个模型的预测结果的决定系数\n",
    "score = performance_metric([3, -0.5, 2, 7, 4.2], [2.5, 0.0, 2.1, 7.8, 5.3])\n",
    "print \"Model has a coefficient of determination, R^2, of {:.3f}.\".format(score)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 问题 3 - 回答:\n",
    "\n",
    "成功了。\n",
    "\n",
    "从决定系数上来看，达到了0.923,已经很高了，说明这个模型很好地描述了目标变量的变化。\n",
    "\n",
    "从预测结果来看，整体趋势和真实数据是一致的，同时下降或升高。每个预测值与真实值也差得不远，没有特别离谱的预测值，都在可接受区间内。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---\n",
    "## 第四步. 分析模型的表现\n",
    "在项目的第四步，我们来看一下不同参数下，模型在训练集和验证集上的表现。这里，我们专注于一个特定的算法（带剪枝的决策树，但这并不是这个项目的重点），和这个算法的一个参数 `'max_depth'`。用全部训练集训练，选择不同`'max_depth'` 参数，观察这一参数的变化如何影响模型的表现。画出模型的表现来对于分析过程十分有益，这可以让我们看到一些单看结果看不到的行为。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 学习曲线\n",
    "下方区域内的代码会输出四幅图像，它们是一个决策树模型在不同最大深度下的表现。每一条曲线都直观得显示了随着训练数据量的增加，模型学习曲线的在训练集评分和验证集评分的变化，评分使用决定系数R<sup>2</sup>。曲线的阴影区域代表的是该曲线的不确定性（用标准差衡量）。\n",
    "\n",
    "运行下方区域中的代码，并利用输出的图形回答下面的问题。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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tepfmzKnrXZo40UYUbNxow/E2brRhe3PnJt9WaqqNPsjNtZ4mvx+2bLGpyD/7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1+2vo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\nRK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f7f7d305ed0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 根据不同的训练集大小，和最大深度，生成学习曲线\n",
    "vs.ModelLearning(X_train, y_train)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 问题 4 - 学习曲线\n",
    "*选择上述图像中的其中一个，并给出其最大深度。随着训练数据量的增加，训练集曲线的评分有怎样的变化？验证集曲线呢？如果有更多的训练数据，是否能有效提升模型的表现呢？*\n",
    "\n",
    "**提示：**学习曲线的评分是否最终会收敛到特定的值？"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 问题 4 - 回答:\n",
    "对于max_depth = 3的情况，随着训练数据量的增加，训练集曲线的评分平稳下降，最后趋于稳定，收敛到0.8；验证集曲线稳步上升，最后也趋于稳定，收敛到0.7，并且与训练集曲线越来越接近。\n",
    "\n",
    "该模型的复杂程度适应数据规模，可以很好地泛化，既没有因为过于简单无法提取出足够特征，也没有因为过于复杂而产生过拟合。\n",
    "\n",
    "因此，该模型已经训练饱和，再增加训练数据，也只可能提升非常细微的表现，并不能有效提升模型的表现。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 复杂度曲线\n",
    "下列代码内的区域会输出一幅图像，它展示了一个已经经过训练和验证的决策树模型在不同最大深度条件下的表现。这个图形将包含两条曲线，一个是训练集的变化，一个是验证集的变化。跟**学习曲线**相似，阴影区域代表该曲线的不确定性，模型训练和测试部分的评分都用的 `performance_metric` 函数。\n",
    "\n",
    "运行下方区域中的代码，并利用输出的图形并回答下面的两个问题。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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zyPHl4Pf4O2UGUrJDtu/sPsDHxpjVACJyP3A0kCl8Bih21kuAXswKJesEg7B6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\nYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f7f7d37ffd0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 根据不同的最大深度参数，生成复杂度曲线\n",
    "vs.ModelComplexity(X_train, y_train)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 问题 5 - 偏差（bias）与方差（variance）之间的权衡取舍\n",
    "*当模型以最大深度 1训练时，模型的预测是出现很大的偏差还是出现了很大的方差？当模型以最大深度10训练时，情形又如何呢？图形中的哪些特征能够支持你的结论？*\n",
    "  \n",
    "**提示：** 你如何得知模型是否出现了偏差很大或者方差很大的问题？"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 问题 5 - 回答:\n",
    "模型以最大深度1训练时，模型的预测出现了很大的偏差，因为训练集的r2得分只有0.45左右，说明模型太过简单，无法表示数据的关系。\n",
    "\n",
    "模型以最大深度10训练时，产生了过拟合，模型预测的偏差很小，因为训练集的r2得分几乎到了1 ，说明很好地学习了训练集的特征，但是对于测试集的r2得分只有0.6 并且有下降的趋势，出现了方差很大的问题。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 问题 6- 最优模型的猜测\n",
    "*结合问题 5 中的图，你认为最大深度是多少的模型能够最好地对未见过的数据进行预测？你得出这个答案的依据是什么？*"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 问题 6 - 回答:\n",
    "\n",
    "我认为最大深度是 4 的模型，能够最好地对未见过的数据进行预测。\n",
    "\n",
    "因为最大深度为 4 是测试集的拐点，说明此时测试集的r2得分最好，也是偏差和方差取得平衡的地方。虽然比起最大深度为 3 的情况，训练集和测试集的差距变大了，但是可以通过更多数据的训练来解决。因此，最大深度为 4 的模型能最好地对未见过的数据进行预测。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---\n",
    "## 第五步. 选择最优参数"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 问题 7- 网格搜索（Grid Search）\n",
    "*什么是网格搜索法？如何用它来优化模型？*\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 问题 7 - 回答:\n",
    "\n",
    "网格搜索法就是遍历多个参数的各种可能取值，训练多个模型，选择评分最高的模型，从而实现自动选择最优参数。\n",
    "\n",
    "要用网格搜索法优化模型，需要设置每个参数的各种可能取值，以及评分的标准，然后让计算机自动尝试每一种参数组合，训练模型。最后得出评分最高的参数组合，就可以用来作为优化模型的参考。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 问题 8 - 交叉验证\n",
    "- 什么是K折交叉验证法（k-fold cross-validation）？\n",
    "- [GridSearchCV](http://scikit-learn.org/stable/modules/generated/sklearn.model_selection.GridSearchCV.html)是如何结合交叉验证来完成对最佳参数组合的选择的？\n",
    "- [GridSearchCV](http://scikit-learn.org/stable/modules/generated/sklearn.model_selection.GridSearchCV.html)中的`'cv_results_'`属性能告诉我们什么？\n",
    "- 网格搜索时如果不使用交叉验证会有什么问题？交叉验证又是如何解决这个问题的？\n",
    "\n",
    "**提示：** 在下面 fit_model函数最后加入 `print pd.DataFrame(grid.cv_results_)` 可以帮你查看更多信息。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 问题 8 - 回答：\n",
    "\n",
    "- k折交叉验证法是将训练集分成k份，每次选择一份作为验证集，其他进行训练。重复k次后，得到的结果再取平均。这样，相当于整个训练集都被用于训练和验证，从而得到更客观的评分。\n",
    "\n",
    "- GridSearchCV 中的 cv 参数表示交叉验证的策略，利用交叉验证得到的评分能避免特征分布不均造成的评分误差，因此选择交叉验证的评分作为标准，评分最高的参数组合可以认为是最佳参数组合。\n",
    "\n",
    "- GridSearchCV 中的 'cv results' 能告诉我们，每组参数的具体参数、训练得分、测试得分、得分排名、训练用时、测试用时。\n",
    "\n",
    "- 网格搜索时，如果不使用交叉验证，评分的标准很容易受特征分布的影响。如果特征分布不均，不使用交叉验证，那么评分随机性很大，并不能帮助网格搜索找到最好的参数。使用了交叉验证，因为利用了整个训练集进行训练和验证，并且最终结果进行了平均，评分的波动随机性很小，更能反映真实情况，帮助网格搜索找到最优参数组合。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 编程练习 4：训练最优模型\n",
    "在这个练习中，你将需要将所学到的内容整合，使用**决策树算法**训练一个模型。为了得出的是一个最优模型，你需要使用网格搜索法训练模型，以找到最佳的 `'max_depth'` 参数。你可以把`'max_depth'` 参数理解为决策树算法在做出预测前，允许其对数据提出问题的数量。决策树是**监督学习算法**中的一种。\n",
    "\n",
    "在下方 `fit_model` 函数中，你需要做的是：\n",
    "1. **定义 `'cross_validator'` 变量**: 使用 `sklearn.model_selection` 中的 [`KFold`](http://scikit-learn.org/stable/modules/generated/sklearn.model_selection.KFold.html) 创建一个交叉验证生成器对象;\n",
    "2. **定义 `'regressor'` 变量**: 使用  `sklearn.tree` 中的 [`DecisionTreeRegressor`](http://scikit-learn.org/stable/modules/generated/sklearn.tree.DecisionTreeRegressor.html) 创建一个决策树的回归函数;\n",
    "3. **定义 `'params'` 变量**: 为 `'max_depth'` 参数创造一个字典，它的值是从1至10的数组;\n",
    "4. **定义 `'scoring_fnc'` 变量**: 使用 `sklearn.metrics` 中的 [`make_scorer`](http://scikit-learn.org/stable/modules/generated/sklearn.metrics.make_scorer.html)  创建一个评分函数；\n",
    " 将 `‘performance_metric’` 作为参数传至这个函数中；\n",
    "5. **定义 `'grid'` 变量**: 使用 `sklearn.model_selection` 中的 [`GridSearchCV`](http://scikit-learn.org/stable/modules/generated/sklearn.model_selection.GridSearchCV.html) 创建一个网格搜索对象；将变量`'regressor'`, `'params'`, `'scoring_fnc'`和 `'cross_validator'` 作为参数传至这个对象构造函数中；\n",
    "  \n",
    "如果你对python函数的默认参数定义和传递不熟悉，可以参考这个MIT课程的[视频](http://cn-static.udacity.com/mlnd/videos/MIT600XXT114-V004200_DTH.mp4)。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# TODO 4\n",
    "\n",
    "#提示: 导入 'KFold' 'DecisionTreeRegressor' 'make_scorer' 'GridSearchCV' \n",
    "from sklearn.model_selection import KFold\n",
    "from sklearn.tree import DecisionTreeRegressor\n",
    "from sklearn.metrics import make_scorer\n",
    "from sklearn.model_selection import GridSearchCV\n",
    "\n",
    "def fit_model(X, y):\n",
    "    \"\"\" 基于输入数据 [X,y]，利于网格搜索找到最优的决策树模型\"\"\"\n",
    "    \n",
    "    cross_validator = KFold()\n",
    "    \n",
    "    regressor = DecisionTreeRegressor()\n",
    "\n",
    "    params = {'max_depth':[1, 2, 3, 4, 5, 6, 7, 8, 9 ,10]}\n",
    "\n",
    "    scoring_fnc = make_scorer(performance_metric)\n",
    "\n",
    "    grid = GridSearchCV(regressor, params, scoring_fnc, cv = cross_validator)\n",
    "\n",
    "    # 基于输入数据 [X,y]，进行网格搜索\n",
    "    grid = grid.fit(X, y)\n",
    "\n",
    "    # 返回网格搜索后的最优模型\n",
    "    return grid.best_estimator_"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 编程练习 4：训练最优模型 （可选）\n",
    "在这个练习中，你将需要将所学到的内容整合，使用**决策树算法**训练一个模型。为了得出的是一个最优模型，你需要使用网格搜索法训练模型，以找到最佳的 `'max_depth'` 参数。你可以把`'max_depth'` 参数理解为决策树算法在做出预测前，允许其对数据提出问题的数量。决策树是**监督学习算法**中的一种。\n",
    "\n",
    "在下方 `fit_model` 函数中，你需要做的是：\n",
    "\n",
    "- 遍历参数`‘max_depth’`的可选值 1～10，构造对应模型\n",
    "- 计算当前模型的交叉验证分数\n",
    "- 返回最优交叉验证分数对应的模型"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# TODO 4 可选\n",
    "\n",
    "'''\n",
    "不允许使用 DecisionTreeRegressor 以外的任何 sklearn 库\n",
    "\n",
    "提示: 你可能需要实现下面的 cross_val_score 函数\n",
    "\n",
    "def cross_val_score(estimator, X, y, scoring = performance_metric, cv=3):\n",
    "    \"\"\" 返回每组交叉验证的模型分数的数组 \"\"\"\n",
    "    scores = [0,0,0]\n",
    "    return scores\n",
    "'''\n",
    "\n",
    "from sklearn.tree import DecisionTreeRegressor\n",
    "\n",
    "def cross_val_score(estimator, X, y, scoring = performance_metric, cv=3):\n",
    "    \"\"\" 返回每组交叉验证的模型分数的数组 \"\"\"\n",
    "    \n",
    "    scores = [0,0,0]\n",
    "    # split X, y\n",
    "    X_div = len(X) / 3\n",
    "    X_K = [X.iloc[:X_div], X.iloc[X_div: 2 * X_div], X.iloc[2 * X_div:]]\n",
    "    y_div = len(y) / 3\n",
    "    y_K = [y.iloc[:y_div], y.iloc[y_div: 2 * y_div], y.iloc[2 * y_div:]]\n",
    "    \n",
    "    for i in range(3):\n",
    "        # use X_K[i] for test and other for train\n",
    "        estimator.fit(pd.concat([X_K[(i + 1) % 3], X_K[(i + 2) % 3]]),\n",
    "                      pd.concat([y_K[(i + 1) % 3], y_K[(i + 2) % 3]]))\n",
    "        pred = estimator.predict(X_K[i])\n",
    "        #print pred, y_K\n",
    "        scores[i] = scoring(y_K[i], pred)   \n",
    "    \n",
    "    return scores\n",
    "\n",
    "def fit_model2(X, y):\n",
    "    \"\"\" 基于输入数据 [X,y]，利于网格搜索找到最优的决策树模型\"\"\"\n",
    "    \n",
    "    params = {'max_depth':[1, 2, 3, 4, 5, 6, 7, 8, 9 ,10]}\n",
    "    \n",
    "    max_score = 0\n",
    "    \n",
    "    for param in params['max_depth']:\n",
    "        estimator = DecisionTreeRegressor(max_depth = param)\n",
    "        score = sum(cross_val_score(estimator, X, y)) / 3\n",
    "        print score\n",
    "        if(score > max_score):\n",
    "            #最优交叉验证分数对应的最优模型\n",
    "            best_estimator = estimator\n",
    "            max_score = score\n",
    "    \n",
    "    return best_estimator"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.354578582793\n",
      "0.615499502463\n",
      "0.73979580228\n",
      "0.755134163783\n",
      "0.730134290045\n",
      "0.71841617962\n",
      "0.682132273713\n",
      "0.679954173812\n",
      "0.668840803585\n",
      "0.666507045001\n",
      "Parameter 'max_depth' is 4 for the optimal model.\n"
     ]
    }
   ],
   "source": [
    "# 基于训练数据，获得最优模型\n",
    "optimal_reg = fit_model2(X_train, y_train)\n",
    "\n",
    "# 输出最优模型的 'max_depth' 参数\n",
    "print \"Parameter 'max_depth' is {} for the optimal model.\".format(optimal_reg.get_params()['max_depth'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 问题 9 - 最优模型\n",
    "*最优模型的最大深度（maximum depth）是多少？此答案与你在**问题 6**所做的猜测是否相同？*\n",
    "\n",
    "运行下方区域内的代码，将决策树回归函数代入训练数据的集合，以得到最优化的模型。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Parameter 'max_depth' is 4 for the optimal model.\n"
     ]
    }
   ],
   "source": [
    "# 基于训练数据，获得最优模型\n",
    "optimal_reg = fit_model(X_train, y_train)\n",
    "\n",
    "# 输出最优模型的 'max_depth' 参数\n",
    "print \"Parameter 'max_depth' is {} for the optimal model.\".format(optimal_reg.get_params()['max_depth'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 问题 9 - 回答：\n",
    "\n",
    "最优模型的最大深度为 4。\n",
    "\n",
    "与我在问题 6 的猜测相同。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 第六步. 做出预测\n",
    "当我们用数据训练出一个模型，它现在就可用于对新的数据进行预测。在决策树回归函数中，模型已经学会对新输入的数据*提问*，并返回对**目标变量**的预测值。你可以用这个预测来获取数据未知目标变量的信息，这些数据必须是不包含在训练数据之内的。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 问题 10 - 预测销售价格\n",
    "想像你是一个在波士顿地区的房屋经纪人，并期待使用此模型以帮助你的客户评估他们想出售的房屋。你已经从你的三个客户收集到以下的资讯:\n",
    "\n",
    "| 特征 | 客戶 1 | 客戶 2 | 客戶 3 |\n",
    "| :---: | :---: | :---: | :---: |\n",
    "| 房屋内房间总数 | 5 间房间 | 4 间房间 | 8 间房间 |\n",
    "| 社区贫困指数（％被认为是贫困阶层） | 17% | 32% | 3% |\n",
    "| 邻近学校的学生-老师比例 | 15：1 | 22：1 | 12：1 |\n",
    "\n",
    "*你会建议每位客户的房屋销售的价格为多少？从房屋特征的数值判断，这样的价格合理吗？为什么？* \n",
    "\n",
    "**提示：**用你在**分析数据**部分计算出来的统计信息来帮助你证明你的答案。\n",
    "\n",
    "运行下列的代码区域，使用你优化的模型来为每位客户的房屋价值做出预测。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Predicted selling price for Client 1's home: $423,913.64\n",
      "Predicted selling price for Client 2's home: $238,000.00\n",
      "Predicted selling price for Client 3's home: $913,080.00\n"
     ]
    }
   ],
   "source": [
    "# 生成三个客户的数据\n",
    "client_data = [[5, 17, 15], # 客户 1\n",
    "               [4, 32, 22], # 客户 2\n",
    "               [8, 3, 12]]  # 客户 3\n",
    "\n",
    "# 进行预测\n",
    "predicted_price = optimal_reg.predict(client_data)\n",
    "for i, price in enumerate(predicted_price):\n",
    "    print \"Predicted selling price for Client {}'s home: ${:,.2f}\".format(i+1, price)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 问题 10 - 回答：\n",
    "\n",
    "我会建议客户 1、2、3 的房屋销售价格分别为 \\$423,913，\\$238,000，\\$913,080。\n",
    "\n",
    "根据之前对数据的分析，这些是合理的价格。\n",
    "\n",
    "- 从房间数来看，应该是房间数越多，售价越高，预测的价格符合这个规律。\n",
    "\n",
    "- 从社区贫困指数来看，贫困指数越高，售价越低，预测的价格也符合这个趋势。\n",
    "\n",
    "- 从临近学校的学生老师比例来看，每个老师对应学生越少，教育资源相对更丰富，相应地段的房屋售价也越高，预测的价格也符合这个趋势。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 编程练习 5\n",
    "你刚刚预测了三个客户的房子的售价。在这个练习中，你将用你的最优模型在整个测试数据上进行预测, 并计算相对于目标变量的决定系数 R<sup>2</sup>的值**。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Optimal model has R^2 score 0.82 on test data\n"
     ]
    }
   ],
   "source": [
    "#TODO 5\n",
    "\n",
    "# 提示：你可能需要用到 X_test, y_test, optimal_reg, performance_metric\n",
    "# 提示：你可能需要参考问题10的代码进行预测\n",
    "# 提示：你可能需要参考问题3的代码来计算R^2的值\n",
    "pred = optimal_reg.predict(X_test)\n",
    "\n",
    "r2 = performance_metric(y_test, pred)\n",
    "\n",
    "print \"Optimal model has R^2 score {:,.2f} on test data\".format(r2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 问题11 - 分析决定系数\n",
    "\n",
    "你刚刚计算了最优模型在测试集上的决定系数，你会如何评价这个结果？"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 问题11 - 回答\n",
    "\n",
    "决定系数为 0.82，达到了 0.8，这是一个相当好的决定系数。说明模型对于陌生的数据依然能给出很好的预测。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 模型健壮性\n",
    "\n",
    "一个最优的模型不一定是一个健壮模型。有的时候模型会过于复杂或者过于简单，以致于难以泛化新增添的数据；有的时候模型采用的学习算法并不适用于特定的数据结构；有的时候样本本身可能有太多噪点或样本过少，使得模型无法准确地预测目标变量。这些情况下我们会说模型是欠拟合的。\n",
    "\n",
    "### 问题 12 - 模型健壮性\n",
    "\n",
    "模型是否足够健壮来保证预测的一致性？\n",
    "\n",
    "**提示**: 执行下方区域中的代码，采用不同的训练和测试集执行 `fit_model` 函数10次。注意观察对一个特定的客户来说，预测是如何随训练数据的变化而变化的。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Trial 1: $411,000.00\n",
      "Trial 2: $411,417.39\n",
      "Trial 3: $415,800.00\n",
      "Trial 4: $428,316.00\n",
      "Trial 5: $413,334.78\n",
      "Trial 6: $411,931.58\n",
      "Trial 7: $399,663.16\n",
      "Trial 8: $407,232.00\n",
      "Trial 9: $402,531.82\n",
      "Trial 10: $413,700.00\n",
      "\n",
      "Range in prices: $28,652.84\n"
     ]
    }
   ],
   "source": [
    "# 请先注释掉 fit_model 函数里的所有 print 语句\n",
    "vs.PredictTrials(features, prices, fit_model, client_data)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 问题 12 - 回答：\n",
    "\n",
    "随着训练数据的变化，预测值在 \\$410,000.00 上下波动，波动范围在 8%以内，并没有离谱的预测值，因此可以认为预测基本一致。所以，这个模型足够健壮。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 问题 13 - 实用性探讨\n",
    "*简单地讨论一下你建构的模型能否在现实世界中使用？* \n",
    "\n",
    "提示：回答以下几个问题，并给出相应结论的理由：\n",
    "- *1978年所采集的数据，在已考虑通货膨胀的前提下，在今天是否仍然适用？*\n",
    "- *数据中呈现的特征是否足够描述一个房屋？*\n",
    "- *在波士顿这样的大都市采集的数据，能否应用在其它乡镇地区？*\n",
    "- *你觉得仅仅凭房屋所在社区的环境来判断房屋价值合理吗？*"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 问题 13 - 回答：\n",
    "\n",
    "这个模型仅适用特定时间空间，特定对象的预测。\n",
    "\n",
    "- 对于1978年所采集的数据，即使已经考虑通货膨胀，也不适用今天。整个房地产行业在 30 年间变化了很多，人们的观念也改变了很多，因此并不适用。\n",
    "\n",
    "- 数据中呈现的特征不足以描述一个房屋。特征有房间数、社区贫困指数、师生比，但是每个房间有多大、装修如何、交通便利程度、配套设施、夜晚安静程度等等特征都没有，因此不够描述一个房屋。\n",
    "\n",
    "- 在波士顿大都市采集的数据，不能应用在其他乡镇地区。在大都市，人们购买房屋时可能更考虑周围的环境，因此师生比可能有较高的权值，而乡镇地区，可能更考虑房屋本身，因此房间数可能有较高的权值。这不是同一种类的对象，因此不能应用。\n",
    "\n",
    "- 我觉得仅凭房屋所在社区的环境来判断房屋价值不合理。社区环境固然是房屋价值很重要的一部分，但是房屋本身的属性也很重要。最明显的就是房屋的面积，房屋面积越大，一般价格也成线性地增大。此外还有房屋的装修、建造年限等等因素，都会影响房屋的价值。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 可选问题 - 预测北京房价\n",
    "\n",
    "（本题结果不影响项目是否通过）通过上面的实践，相信你对机器学习的一些常用概念有了很好的领悟和掌握。但利用70年代的波士顿房价数据进行建模的确对我们来说意义不是太大。现在你可以把你上面所学应用到北京房价数据集中 `bj_housing.csv`。\n",
    "\n",
    "免责声明：考虑到北京房价受到宏观经济、政策调整等众多因素的直接影响，预测结果仅供参考。\n",
    "\n",
    "这个数据集的特征有：\n",
    "- Area：房屋面积，平方米\n",
    "- Room：房间数，间\n",
    "- Living: 厅数，间\n",
    "- School: 是否为学区房，0或1\n",
    "- Year: 房屋建造时间，年\n",
    "- Floor: 房屋所处楼层，层\n",
    "\n",
    "目标变量：\n",
    "- Value: 房屋人民币售价，万\n",
    "\n",
    "你可以参考上面学到的内容，拿这个数据集来练习数据分割与重排、定义衡量标准、训练模型、评价模型表现、使用网格搜索配合交叉验证对参数进行调优并选出最佳参数，比较两者的差别，最终得出最佳模型对验证集的预测分数。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# TODO 6\n",
    "\n",
    "# 你的代码\n",
    "\n",
    "bj_data = pd.read_csv('bj_housing.csv')\n",
    "value = bj_data['Value']\n",
    "area = bj_data['Area']\n",
    "room = bj_data['Room']\n",
    "living = bj_data['Living']\n",
    "school = bj_data['School']\n",
    "year = bj_data['Year']\n",
    "floor = bj_data['Floor']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x7f7f7d011810>]"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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bZWZfD+njzWyLme0Jr+Uh3czs+2bWYGa/N7O5Sd+1NOTfY2ZL+/pNGX7DNQ30vOnl3POZ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\nYkoBQEQkphQARERiSgFARCSm/j/MT45GcgPReQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f7f7d0a5a10>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(area, value, '.')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "基本趋势是面积越大，价格越高。但是最右和最上，似乎是异常值，需要进一步验证。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x7f7f7cfc39d0>]"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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aIzd95hP+8reKVLIH0AX8l4jYJOl0oFXSOuCrwJMRsVTSEmAJ8E1gPqULvs8AZgN3AbMl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\nRK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f7f7d039c10>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(room, value, '.')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "房间数1～4时，基本趋势是增大，但是之后又下降，可能是因为有些房子很小但还是分成了很多小房间，因此房价并不高。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x7f7f7cfee410>]"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f7f7d2f0ed0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(living, value, '.')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "厅数和房间数趋势差不多。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x7f7f7cfe5490>]"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f7f7c4cc050>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(school, value, '.')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "设想中学区房应该总体会更贵一些，但是从数据来看，总体差不多。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x7f7f7cfe5110>]"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f7f7c41b790>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(year, value, '.')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "发现有一个1000年的异常值，应该去除。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Area</th>\n",
       "      <th>Value</th>\n",
       "      <th>Room</th>\n",
       "      <th>Living</th>\n",
       "      <th>School</th>\n",
       "      <th>Year</th>\n",
       "      <th>Floor</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>7761</th>\n",
       "      <td>94</td>\n",
       "      <td>205</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1014</td>\n",
       "      <td>32</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      Area  Value  Room  Living  School  Year  Floor\n",
       "7761    94    205     3       2       1  1014     32"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bj_data[bj_data['Year']<1500]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "面积和价值在正常值，因此不用重新观测之前的特征。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "bj_data = bj_data.drop(labels = 7761, axis = 0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Area</th>\n",
       "      <th>Value</th>\n",
       "      <th>Room</th>\n",
       "      <th>Living</th>\n",
       "      <th>School</th>\n",
       "      <th>Year</th>\n",
       "      <th>Floor</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Empty DataFrame\n",
       "Columns: [Area, Value, Room, Living, School, Year, Floor]\n",
       "Index: []"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bj_data[bj_data['Year']<1500]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "year = bj_data['Year']\n",
    "value = bj_data['Value']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x7f7f7cf82a10>]"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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7t8wuT1sd1J6UNuKsYjo6YhPSHVZQ6BcnTqUPxgNJgUGkH6QqRcDpv9ZLzy1h\n5DnDIwO8Uk0JvfvQ6VXGrp0+np+ur8toANe0shE0vdfaOS9R3JdWboyct2bzns4GT6f7kcOtp9op\nwBmrh5RCWSdiyAaGQhtwJGee5FJEYj/1Do+Ndi0yKBt5Fu8cOp6yHWLWlNGd2w/89u20QeH97zuP\nCy84m13Nx7qdwrolqZrp6Ml2rp9xIdPGjWTO1DH87OWdaQekKSj0v6Z3W/nA/3qaWVNG8/Di2XnL\nx5Acx5C4rN/Xn9wy4P3IZfAYyHWIE/upQ6yLYrvH+u2/srMFM6PYYgGjpNj4xLSxkYfD/iPRKSAm\njjqbD0+6gGFFUNf0Lq/sbOb7N8zodl2DkuKuaxH/cvM7HD1+ipkXlTLr4sExWd9QduJUB+u3H+C2\nFZV5y8OQLDGkm7NFJNFAjzSNjyFIV6dfdl4Jt141JW1XxeR2izs/OY2WY61s2XM44+m5hxUXcbK9\naz32MzWNlI8Z0Tn2QvLvjBrgZmbzgB8BxcCD7r4s15+RqvugSLKBXvMgcQxB6bklPLmpgY07T5dU\nrp8xkTs/+f6016dqt6iub8lqUFRR1wIDEGv4Tv6DSvLrwlHn5O2zBzQwmFkxcB9wLdAAbDSzNe7+\nRi4/J13Dn0iifKx5kDiG4ObZ5RkvbRmX3G6R7fTct8y+KOVspdPGjWTWuSWRP6ggVq11wbnDI2Mj\nkk0adXaXmU6lb4oMvn/DjLx9vrkPXCu4mV0FfNPdrwv7XwNw9/+d6vyKigqvqqoasPzJmSffa+vm\nQ+I6yRCb2jlejbaqchePbdzFG3uP0NHhkWN3rd7EszX7OJ7QpfLbn/lQZ8llQ91Bvvfsm53Hzh5e\nxKwpo3np7QO9mtxusM6U2heJHQH68v/RzKrdvaLX1w9wYPgsMM/dvxj2bwVmu/uXU52vwCDSf7Kd\nkymutz3+4tdNn3B+57QPP/rNW7yys5kPjBvJtdPHd/tATFwadPz5Z0dKWoklr/IxI3h6614aDx1n\n96HjkR4+ySW0+P7wIutSIhp1zjBGnVvCvOnjWb1xF4eOt6Vc82HSqLN55/CJSLvRtLIR1DcfozVh\nMMh5ZxXz3y4bR82ew7x94L3I+VPGnJtxqTETQy4wmNkSYAlAeXn5zPr6+gHLn4icuZLXpO5OquCY\nKi1fJdLBFhhUlSQi0s/6GhgGehzDRmCamV1sZiXATcCaAc6DiIh0Y0B7Jbl7m5l9GXiWWHfVh9y9\nZiDzICIi3RvwcQzuvhZYO9CfKyIimRmSU2KIiEjvKTCIiEiEAoOIiEQMaHfVbJlZE5A4kGEskH7l\n8cI2WPM+WPMNynu+KO/5kZjX2SpVAAAGJ0lEQVT3i9y9rLuTu1PQgSGZmVX1pW9uPg3WvA/WfIPy\nni/Ke37kMu+qShIRkQgFBhERiRhsgWF5vjPQB4M174M136C854vynh85y/ugamMQEZH+N9hKDCIi\n0s/yGhjM7CEz229mWxPSZpjZBjPbbGZVZjYrpF9gZv9hZq+ZWY2Z3Z5wzSIz2x5+FuUx7x82s5fN\nbEvI6/kJx75mZrVm9qaZXZeQPi+k1ZrZ0kLLu5lda2bVIb3azD6VcM3MkF5rZj82szQLR+Yn7wnH\ny83sXTP7u4S0gr7v4dgfhmM14fjZIX1A73uW/1+Gm9nKkL4tPoNyOJaPez7ZzJ43szfCffxKSB9t\nZuvCM2OdmZWGdAv3tNbMXjezKxPea0CfM73I+y0hz1vM7CUz+3DCe2V37909bz/AJ4Arga0Jab8G\n5oftBcALYfvrwHfCdhnQDJQAo4G68FoatkvzlPeNwB+H7S8A3wrbHwReA84CLgbeJjaJYHHYnhq+\ny2vABwss71cAF4bty4E9Cde8AswhtgLk0/F/t0LJe8LxJ4CfA38X9gfDfR8GvA58OOyPAYrzcd+z\nzPfNwOqwfS6wE5iSx3s+AbgybI8E3gq/j98Flob0pZx+tiwI99TCPa4M6QP+nOlF3j8azxMwPyHv\nWd/7vJYY3H09sQd8JBmI/9V0AfBOQvrI8NfReeG6NuA6YJ27N7t7C7AOmJenvF8CrA/b64A/D9sL\nif2ynHT3HUAtMCv81Lp7nbu3AqvDuQWTd3ff5O7xf4Ma4BwzO8vMJgDnu/sGj/3vexi4vpDyDmBm\n1wM7Qt7jCv6+A3OB1939tXDtQXdvz8d9zzLfDowws2HAOUArcIT83fO97v5q2D4KbAMmhs9eGU5b\nyel7uBB42GM2AKPCPR/w50y2eXf3l0LeADYAk8J21ve+ENsY7gK+Z2a7gX8B4kXRnwCXEQsUW4Cv\nuHsHsRu1O+H6hpCWDzWcvuF/AUwO2+nyOBjynujPgVfd/SSxfDYkHCu4vJvZecDdwD8lnT8Y7vsl\ngJvZs2b2qpn9Q0gvlPueLt9PAO8Be4FdwL+4ezMFcM/NbAqxEnAlMM7d94ZDjcC4sF2Qv6sZ5j3R\nYmIlH+hF3gsxMPw18DfuPhn4G2BFSL8O2AxcCMwAfpJcl1wAvgD8dzOrJlb0a81zfrLRbd7NbDrw\nHeCOPOStJ+ny/k3gB+7+br4yloF0eR8GfBy4Jbx+xsyuyU8WU0qX71lAO7Hf04uBvzWzqfnJ4mnh\nj4RfAHe5+5HEY6HkVbDdM7PNu5l9klhguLu3nzng6zFkYBHwlbD9c+DBsH07sCzciFoz2wF8ANgD\nXJ1w/STghQHJaRJ3/z2xKgDM7BLg0+HQHqJ/gU8KaXSTPqC6yTtmNgl4ErjN3d8OyXs4XVSFwsz7\nbOCzZvZdYBTQYWYngGoK/743AOvd/UA4tpZYPf//owDuezf5vhl4xt1PAfvN7HdABbG/WPNyz81s\nOLEH6yPu/u8heZ+ZTXD3vaGqaH9IT/e7mpfnTJZ5x8z+kNgzc767HwzJ3T1/UuvPxpMMG1imEG3U\n2gZcHbavAarD9v3E1ouGWNFpD7FJo0YTq0MuDT87gNF5yvv7wmsRsbrfL4T96UQbn+uINQgNC9sX\nc7pRaHqB5X1UyNefpXiP5EbQBYWU96RrvsnpxufBcN9LgVeJNeAOA34DfDpf9z2LfN8N/FvYHgG8\nAfxhvu55uEcPAz9MSv8e0Qbc74btTxNtfH4lpA/4c6YXeS8n1n750aTzs773/f6L0MMXf5RYXeQp\nYn8hLSZWbK4Oma8EZoZzLyTWY2kLsBX4y4T3+UK4IbXA7XnM+1eI9Rx4C1hGGEAYzv9HYj0D3iSh\nFwmxXhBvhWP/WGh5B/4nsTrjzQk/8YdCRfi3eJtYG5AVUt6TrvsmITAMhvsezv9LYnX5W+O//Pm4\n71n+fzmPWEm/hlhQ+Ps83/OPE6tqeT3h/+8CYr28ngO2Ewu6o8P5BtwX8rgFqEh4rwF9zvQi7w8C\nLQnnVvX23mvks4iIRBRi47OIiOSRAoOIiEQoMIiISIQCg4iIRCgwiIhIhAKDiIhEKDCIiEiEAoOI\niET8f3nkt8w86oy/AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f7f7c436ad0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(year, value, '.')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "又有一个1880年的。。。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Area</th>\n",
       "      <th>Value</th>\n",
       "      <th>Room</th>\n",
       "      <th>Living</th>\n",
       "      <th>School</th>\n",
       "      <th>Year</th>\n",
       "      <th>Floor</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>3108</th>\n",
       "      <td>42</td>\n",
       "      <td>180</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1885</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3230</th>\n",
       "      <td>42</td>\n",
       "      <td>180</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1885</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      Area  Value  Room  Living  School  Year  Floor\n",
       "3108    42    180     1       1       1  1885      6\n",
       "3230    42    180     1       1       1  1885      6"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bj_data[bj_data['Year']<1900]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "面积和价值在正常值，因此不用重新观测之前的特征。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "bj_data = bj_data.drop(labels = [3108, 3230], axis = 0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Area</th>\n",
       "      <th>Value</th>\n",
       "      <th>Room</th>\n",
       "      <th>Living</th>\n",
       "      <th>School</th>\n",
       "      <th>Year</th>\n",
       "      <th>Floor</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Empty DataFrame\n",
       "Columns: [Area, Value, Room, Living, School, Year, Floor]\n",
       "Index: []"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bj_data[bj_data['Year']<1900]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "year = bj_data['Year']\n",
    "floor = bj_data['Floor']\n",
    "value = bj_data['Value']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x7f7f7cf57550>]"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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/eeRLAUBEBpRi9ujpjVIef6BGYBEZUPKZ16eU3bdmO9evWMN9a7YXuyh6AhCRgSWfeX1K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\nQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f7f7cf1d610>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(year, value, '.')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "终于可以看出趋势了，大概增长到2005年，再下降。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x7f7f7cf4ba50>]"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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b1UR83EBbRxdl6f1HB6m3wJNsb9o01PG3397RxdSxI2k+cjLl/kzvJdMgtnRd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\nEfX/ASoRDppFr1WMAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f7f7d0c1f90>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(floor, value, '.')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "大于80楼的，和其他数据实在相差太远，认为是异常值，去除。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Area</th>\n",
       "      <th>Value</th>\n",
       "      <th>Room</th>\n",
       "      <th>Living</th>\n",
       "      <th>School</th>\n",
       "      <th>Year</th>\n",
       "      <th>Floor</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>3624</th>\n",
       "      <td>63</td>\n",
       "      <td>300</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1995</td>\n",
       "      <td>91</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5821</th>\n",
       "      <td>47</td>\n",
       "      <td>225</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1995</td>\n",
       "      <td>91</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5883</th>\n",
       "      <td>47</td>\n",
       "      <td>225</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1995</td>\n",
       "      <td>91</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6030</th>\n",
       "      <td>60</td>\n",
       "      <td>285</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1995</td>\n",
       "      <td>91</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      Area  Value  Room  Living  School  Year  Floor\n",
       "3624    63    300     2       1       1  1995     91\n",
       "5821    47    225     1       1       1  1995     91\n",
       "5883    47    225     1       1       1  1995     91\n",
       "6030    60    285     2       1       1  1995     91"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bj_data[bj_data['Floor'] > 40]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "bj_data = bj_data.drop(labels = [3624, 5821, 5883, 6030], axis = 0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Area</th>\n",
       "      <th>Value</th>\n",
       "      <th>Room</th>\n",
       "      <th>Living</th>\n",
       "      <th>School</th>\n",
       "      <th>Year</th>\n",
       "      <th>Floor</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Empty DataFrame\n",
       "Columns: [Area, Value, Room, Living, School, Year, Floor]\n",
       "Index: []"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bj_data[bj_data['Floor'] > 40]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "floor = bj_data['Floor']\n",
    "value = bj_data['Value']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x7f7f7cf8d6d0>]"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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NAkvdqOQaT0vatvCxxW0sadsybO8hMtzUApC6Uak1nrTEttQLJQCpG5WaJaQltqVepLkn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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f7f7c436e10>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(floor, value, '.')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "最底层和超过30的高层，价格普遍偏低。因为最底层湿气重，最高层不方便。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "接下来开始构建模型，获取特征和目标。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "features = bj_data.drop([\"Value\"] ,axis = 1)\n",
    "value = bj_data['Value']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "分割训练集和测试集。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "X_train, X_test, y_train, y_test = train_test_split(features, value, test_size = 0.2, random_state = 231)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "选择适当复杂度的模型。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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aPrfffnvvO++8c5+yt0bXr0kCmDHD3ql86aVol0QppVRsLFx8sU0FfvfdsGcP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\nOAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f7f7d1577d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 根据不同的训练集大小，和最大深度，生成学习曲线\n",
    "vs.ModelLearning(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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WrbOCFgzCMcfA+edby27mTBu5GQ5bkRSx/XczZ9qxdDqOTnFIppLEU3ESqQTJ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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f7f7cdcf450>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 根据不同的最大深度参数，生成复杂度曲线\n",
    "vs.ModelComplexity(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Parameter 'max_depth' is 8 for the optimal model.\n"
     ]
    }
   ],
   "source": [
    "# 基于训练数据，获得最优模型\n",
    "optimal_reg = fit_model(X_train, y_train)\n",
    "\n",
    "# 输出最优模型的 'max_depth' 参数\n",
    "print \"Parameter 'max_depth' is {} for the optimal model.\".format(optimal_reg.get_params()['max_depth'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "比波士顿的模型，复杂度大了许多，毕竟多了更多的特征。但是训练集和测试集的得分相差太多，感觉不是太稳定的模型。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Optimal model has R^2 score 0.73 on test data\n"
     ]
    }
   ],
   "source": [
    "#TODO 5\n",
    "\n",
    "# 提示：你可能需要用到 X_test, y_test, optimal_reg, performance_metric\n",
    "# 提示：你可能需要参考问题10的代码进行预测\n",
    "# 提示：你可能需要参考问题3的代码来计算R^2的值\n",
    "pred = optimal_reg.predict(X_test)\n",
    "\n",
    "r2 = performance_metric(y_test, pred)\n",
    "\n",
    "print \"Optimal model has R^2 score {:,.2f} on test data\".format(r2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "r2得分有0.73,看上去是可以接受的模型"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "测试模型的健壮性，看看特定房屋的预测值是否稳定。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Area</th>\n",
       "      <th>Value</th>\n",
       "      <th>Room</th>\n",
       "      <th>Living</th>\n",
       "      <th>School</th>\n",
       "      <th>Year</th>\n",
       "      <th>Floor</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>128</td>\n",
       "      <td>370</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2004</td>\n",
       "      <td>21</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>68</td>\n",
       "      <td>330</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>2000</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>125</td>\n",
       "      <td>355</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>2003</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>129</td>\n",
       "      <td>278</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>2005</td>\n",
       "      <td>16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>118</td>\n",
       "      <td>340</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>2003</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Area  Value  Room  Living  School  Year  Floor\n",
       "0   128    370     3       1       1  2004     21\n",
       "1    68    330     1       2       1  2000      6\n",
       "2   125    355     3       2       0  2003      5\n",
       "3   129    278     2       2       0  2005     16\n",
       "4   118    340     3       2       0  2003      6"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bj_data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Trial 1: $488.25\n",
      "Trial 2: $426.05\n",
      "Trial 3: $494.26\n",
      "Trial 4: $471.70\n",
      "Trial 5: $533.26\n",
      "Trial 6: $393.91\n",
      "Trial 7: $625.38\n",
      "Trial 8: $549.02\n",
      "Trial 9: $535.03\n",
      "Trial 10: $510.26\n",
      "\n",
      "Range in prices: $231.47\n"
     ]
    }
   ],
   "source": [
    "# 请先注释掉 fit_model 函数里的所有 print 语句\n",
    "house = features.head().values\n",
    "vs.PredictTrials(features, value, fit_model, house)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "数据波动得很厉害，平均在 $450，波动范围达到了 $230，超过50%，健壮性不够好。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 问题14 - 北京房价预测\n",
    "你成功的用新的数据集构建了模型了吗？他能对测试数据进行验证吗？它的表现是否符合你的预期？交叉验证是否有助于提升你模型的表现？\n",
    "\n",
    "**提示：**如果你是从零开始构建机器学习的代码会让你一时觉得无从下手。这时不要着急，你要做的只是查看之前写的代码，把每一行都看明白，然后逐步构建你的模型。当中遇到什么问题也可以在我们论坛寻找答案。也许你会发现你所构建的模型的表现并没有达到你的预期，这说明机器学习并非是一项简单的任务，构建一个表现良好的模型需要长时间的研究和测试。这也是我们接下来的课程中会逐渐学到的。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 问题14 - 回答\n",
    "\n",
    "- 我成功地用新的数据集构建了模型。\n",
    "\n",
    "- 复用之前的函数，他可以对测试数据进行验证。\n",
    "\n",
    "- 它的表现符合我的预期。在分析数据的时候，就感觉这个数据集对于单个特征的相关性，没有教程里的数据集好。最终r2能达到0.73已经很不错了，不过健壮性的确很差。\n",
    "\n",
    "- 交叉验证的确可以帮助提升模型的表现。从复杂度曲线和学习曲线来看，如果没有交叉验证，往往会找到最复杂的模型，但并不是最适合实际应用的模型。"
   ]
  }
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